Development and calibration of a currency trading strategy using global optimization

We have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.post-prin

LGO : an implementation of a Lipschitzian global optimization procedure

Decision problems are frequently modelled by optimizing the value of a primary objective function under stated feasibility constraints. Specifically, we shall consider here the following global optimization problem: begin{equation min f(x) mbox{ subject to x in D subset RR^n . end{equation We shall assume that in (GOP) $f:D rightarrow RR$ is a continuous function, and D is a bounded, robust subset (`body') in the Euclidean n-space. In addition, the Lipschitz-continuity of f on D will also be postulated, when necessary. The above assumptions define a fairly general class of optimization problems, and typically reflect a paradigm in which a rather vaguely defined, `large' search region is given on which a (potentially) multiextremal function f is minimized. It will also be assumed that the set of global solutions $X^* subset D$ is, at most, countable. To solve (GOP), a general family of adaptive partition strategies can be introduced: consult Pintér (1992a, 1995) and references therein. Necessary and sufficient convergence conditions can be established: these lead to a unified view of numerous GO algorithms, permitting their straightforward generalization and various extensions to handle specific cases of (GOP). The present report discusses a Lipschitzian global optimization program system, for use in the workstation environment at CWI. Implementation aspects are detailed, numerical experience, existing and prospective applications are also highlighted. Application areas include, e.g., the following (Pintér, 1992b, 1995): general (Lipschitzian) nonlinear approximation, systems of nonlinear equations and inequalities, calibration (parameterization) of descriptive system models, data classification, general configuration design, aggregation of negotiated expert opinions, product/mixture design, `black box' design and operation of engineering/environmental systems

Low dimensional simplex evolution: a new heuristic for global optimization

Global optimization, Heuristic, Real-coded, Evolutionary algorithm, Differential evolution, Low dimensional simplex evolution,

A partition-based global optimization algorithm

Global optimization, Partition-based algorithm, DIRECT-type algorithm,